基于特征优化和BSO-RBF神经网络的NOx浓度预测模型OACSTPCD
NOx Concentration Prediction Model Based on Feature Optimization and BSO-RBF Neural Network
针对火力发电厂中燃烧系统运行工况复杂、迟延较大,导致选择性催化还原(SCR)烟气脱硝系统中入口NOx质量浓度难以准确测量的问题,提出了一种基于特征优化和径向基函数(radial basis function,RBF)神经网络的预测模型.将经过特征优化后的变量作为模型的最终输入变量,并使用天牛群优化(beetle swarm optimization,BSO)算法对神经网络超参数进行寻优,建立入口NOx浓度预测模型.结果表明,经过特征优化后的变量放入模型后,其预测结果要优于原始变量:经特征优化及时延处理后的模型其SRMSE减少了 44.5％,R2增加了 2.3％,经过BSO确定后的神经网络超参数使得模型精度也得到了进一步提升.
In the process of thermal power generation,the operation condition of combustion system is complicated and the delay is large,which makes it difficult to accurately measure the inlet NOx mass concentration in the selective catalytic reduction(SCR)flue gas denitration system.To solve this problem,a prediction model based on feature optimization and radial basis(RBF)neural network is proposed.Firstly,the variable after feature optimization is taken as the final input variable of the model.Secondly,the beetle swarm optimization(BSO)is used to optimize the neural network hyperparameters.Finally,a prediction model of inlet NOx concentration is established.The results show that the predictive results of the optimized variables are better than those of the original variables.After feature optimization and timely delay,the SRMSE of the model decreased by 44.5％,and the R2 increased by 2.3％.The neural network hyperparameters determined by BSO also improved the accuracy of the model.
张国兴;王世朋
国能宁夏鸳鸯湖第一发电有限公司,宁夏银川 750011
NO,浓度预测特征优化天牛群优化算法径向基函数神经网络
NOx concentration predictionfeature optimizationbeetle swarm optimization algorithmRBFneural network
《计量学报》 2024 (002)
285-293 / 9
国家重点研发计划(2018YFB0604300)
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